Graph Convolutional Networks (GCNs) [1]} have been garnering increasing attention to researchers in various application fields, owing to their flexible and diversified network architecture that is capable of processing non-grid high-dimensional data. Such properties provide new insight and possibilities in processing hyperspectral data more effectively and efficiently. In detail, GCNs enable the modeling of the relations between data (or samples). Accordingly, this naturally motivates us to use the GCNs to capture the spatial relations of spectral signatures in HSIs. Due to the GCNs' limitations in the graph construction [2]}, particularly for large graphs (need expensive computational cost), GCNs fail to classify or identify materials in large-scale hyperspectral scenes using normal PCs, which leads to relatively less popularity compared to CNN's in HSIC. For this reason, there have been some tentative researches using the GCNs in the HSIC task.
